CN117013610A - Method and equipment for improving large disturbance stability of converter based on particle swarm optimization - Google Patents
Method and equipment for improving large disturbance stability of converter based on particle swarm optimization Download PDFInfo
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Abstract
The invention relates to an operation control technology of an electric power system, in particular to a method and equipment for improving the stability of large disturbance of a current transformer based on a particle swarm algorithm, wherein the method comprises the steps of modeling a typical grid-following type current transformer control structure, comprehensively considering the influence of a power outer loop and a current inner loop on the transient stability of a phase-locked loop, deducing a corresponding relational expression, and providing a design criterion; setting a transient stability optimization target and constraint conditions of the converter by combining design criteria and converter limits; based on the proposed optimization target and constraint conditions, the proportion parameters of the following-net type converter controller are optimized by adopting a particle swarm algorithm. The optimized parameters obtained by the method are favorable for the large disturbance stability of the follow-up grid type converter. On one hand, the method can help to understand the problem of large disturbance stability of the converter, and provides a reference basis for parameter setting of a controller of the converter; on the other hand, the transient stability of the novel power system can be improved, and the novel power system is a foundation for realizing the large-scale access of new energy sources to the power grid.
Description
Technical Field
The invention belongs to the technical field of operation control of electric power systems, and particularly relates to a method and equipment for improving the stability of large disturbance of a current transformer based on a particle swarm algorithm.
Background
The transient stability of a conventional power system is mainly dependent on the dynamic process of the synchronous machine, while the transient stability of a new power system is mainly dependent on the power electronics in which the regulation is fast. The converter is used as a new energy source to be connected into a power electronic interface of a power grid, and a control link of the converter is the core of the stability problem of the novel power system. The existing research on parameter setting of the PI controller of the converter mostly focuses on the stability of small disturbance, adopts a root locus method or adopts an optimization algorithm to solve, wherein the objective function of the optimization algorithm only considers the small disturbance, but does not consider the situation that the system faces the large disturbance.
Part of the literature explores the tuning problem of the parameters of the PI controller of the converter under large disturbance, but only considers the phase-locked loop to ignore the influence of the control strategy, (for example, transient synchronization stability research reviews [ J ] of new energy power systems such as Geng Hua, he Changjun, liu Yushuang, and the like, high voltage technology, 2022, 48 (09): 3367-3383.DOI:10.13336/j.1003-6520. Hve.20221231), or considers the current inner loop control of the converter to ignore the power outer loop. (e.g., xuan, yuan Xiaoming, hu Gubing, research on the influence mechanism of phase-locked loop on the phase-locked loop grid-connected converter active/reactive current excitation-internal potential amplitude/frequency response relation [ J ]. Chinese motor engineering journal, 2023, 43 (10): 3904-3916.Doi:10.13334/j.0258-8013. Pcsee.222339.) in practice, the power outer loop would greatly complicate the system large disturbance stability problem, taking into account.
The current inner ring and the current outer ring are comprehensively considered in literature, but the data is used as a drive, the optimal parameters are determined through a large number of simulations, and the mechanism for setting the parameters of the converter under large disturbance is not clear. (e.g., M.G.Taul, C.Wu, S. -F.Chou and F.Blaabojerg, "Optimal Controller Design for Transient Stability Enhancement of Grid-Following Converters Under Weak-Grid Conditions," in IEEE Transactions on Power Electronics, vol.36, no.9, pp.10251-10264, sept.2021, doi: 10.1109/TPEL.2021.306205.)
Disclosure of Invention
The invention aims to provide a method for improving the stability of large disturbance of a current transformer based on a particle swarm algorithm. Modeling a typical grid-connected converter control structure, comprehensively considering the influence of a power outer loop and a current inner loop on the transient stability of a phase-locked loop, deducing a corresponding relational expression, and providing a design criterion; based on design criteria, setting a transient stability optimization target and constraint conditions of the converter by combining converter limitation; based on the proposed optimization target and constraint conditions, the proportion parameters of the following-net type converter controller are optimized by adopting a particle swarm algorithm.
In order to achieve the above purpose, the invention adopts the following technical scheme: method for improving large disturbance stability of current transformer based on particle swarm optimization, and obtaining the ratio component of internal potential q-axis and current transformation when considering current inner loop and power outer loop by analyzing control model of current transformerRelational expression of the phase angle position of the machine: f (E) q0 ,θ,P ref ,Q ref ) =0, where P ref ,Q ref Respectively setting the active power and the reactive power; analysis to obtain E q0 <0 facilitates large disturbance stability of the system, so that the available objective function isWherein θ is 0 Very close to 0, a set of constraints is (1) constant E in the steady operation interval of the converter q0 <0, thereby avoiding the approach of the system to an unstable balance point as far as possible; (2) f (E) q0 ,θ,P ref ,Q ref ) =0 is a continuous function, thus ensuring that the optimized system has general dynamic characteristics; (3) the parameters of the converter do not exceed the given upper and lower limits, so that the optimized parameters are ensured to be in a reasonable range; finally, a particle swarm optimization algorithm is adopted to obtain a proportional link parameter K of converter control pn (n=1, 2,3, 4), the application of this set of parameters can improve the large disturbance stability of the grid-connected converter. The method comprises the following steps:
step 1, considering the influence of a power outer loop and a current inner loop on a phase-locked loop, modeling a typical grid-following converter control structure, analyzing the influence of a control loop on the transient stability of the grid-following converter, deducing a corresponding relation expression, and providing a design criterion;
step 2, combining design criteria and limitations of the converter, providing a group of available optimization targets and constraint conditions for improving the stability of the large disturbance of the converter, and carrying out specific analysis on specific situations in practical application to properly correct the optimization targets and constraint conditions;
and 3, optimizing the proportional link parameters controlled by the grid-type converter by adopting a particle swarm algorithm according to the set optimization target and constraint conditions, drawing a key variable relation curve of the converter based on an optimization result, and correcting the optimization target and constraint conditions based on an effect curve.
Moreover, the implementation of step 1 includes:
1) A phase-locked loop mathematical model;
the classical phase-locked loop second order model is shown as follows:
wherein θ represents the phase angle of the converter output by the phase-locked loop, the phase angle represents the operating position of the converter, Δω represents the speed difference, V Gq Representing the q-axis component of the terminal voltage, having V Gq ＝V G sin(θ G -θ)，V G For terminal voltage amplitude, θ G For the phase angle of the terminal voltage, K p Represents the phase-locked loop proportional parameter, K i Representing phase-locked loop integration parameters. The phase-locked loop has the principle that: according to input V G Sum phase angle difference calculation V Gq ，V Gq The PI controller generates a speed difference, and an output phase angle is obtained through an integration link.
Defining the amplitude of the internal potential of the current transformer as E and the phase angle as delta, the classical phase-locked loop can be transformed into a phase-locked loop with V Gq For input, θ is the form of output, and the specific transformation mode is as follows:
writing a KVL equation according to a circuit column of an infinite system connected with the converter:
and (3) unfolding:
wherein V is infinite power supply voltage amplitude, the phase angle of the infinite power supply voltage is assumed to be 0 DEG, E is the amplitude of the potential in the converter, theta represents the phase angle of the converter output by the phase-locked loop, namely the angle of the d axis of the phase-locked loop, and the relative angle of the potential in the converter under the coordinate system of the phase-locked loop is delta, X L And X is the sum of the line reactance and the reactance in the converter.
Order the
Then there isA＝V G cosθ G ,B＝V G sinθ G
V Gq The expression of (2) is:
i.e.
The same can be deduced:
wherein V is Gq And theta is the output quantity of the phase-locked loop. E (E) q Is a component of the input quantity, so that its variation causes a variation in the output quantity, and the control loop influences the internal potential q-axis component E q Thereby affecting θ.
2) Current inner loop mathematical model:
the d-axis current inner loop mathematical model is shown as follows:
wherein K is p1 And K i1 Respectively representing the proportional parameter and the integral parameter of the d-axis current loop, I dref Representing the d-axis current set point, X G Representing the reactance in the converter, s is the complex variable in the laplace transform.
Extracting the expression related to the proportion links as follows:
wherein subscript 0 indicates the output of the proportional elementComponent, while subscript 1 indicates the component of the integration element output, such as: the internal potential q-axis component output via the PI controller can be written as E q ＝E q0 +E q1 Subscripts d and q of the variables represent the components in the phase-locked loop reference frame, respectively, e.g., the current has the expression i=i in the phase-locked loop frame d +jI q 。
The mathematical model of the q-axis current inner loop is as follows:
wherein E is d Representing the d-axis component, K, of the internal potential p2 And K i2 Respectively representing the proportional parameter and the integral parameter of the d-axis current loop, I qref Representing the q-axis current setpoint.
Extracting the expression related to the proportion links as follows:
3) Mathematical model of power outer loop:
the power expression including the potential in the converter and its phase angle variables is:
according to ohm's law on the impedance in the converter,
an expression of d-axis current and q-axis current is obtained:
the power output by the converter is as follows:
i.e.
Where P represents the active power output by the converter and Q represents the reactive power.
Substitution of formulas (5) (6) (12) into formula (14) can be obtained
Since the resistance is much smaller than the reactance in the model, the effect of the resistance is ignored, considering the converter port voltage V at the internal potential E G Where the reactive power at the three nodes above V at infinity is equal, then the reactive power output by the converter can be written as effectively
I.e. the reactive power transferred between the potential in the converter and the infinite power source, where current and voltage are expressed in x-y coordinates.
Deriving a mathematical model of the power outer loop from the above expressions of active power and reactive power;
the mathematical model of the active power is:
wherein K is p3 And K i3 The proportional and integral parameters of the active power loop are represented, respectively.
Extracting parts thereof related to only proportional links
The mathematical model of reactive power is:
wherein K is p4 And K i4 The proportional and integral parameters of the reactive power loop are represented, respectively.
Extracting the parts of the parts which are only relevant to the proportion links
4) Obtaining a relational expression of the proportional component of the q-axis of the internal potential and the phase angle position of the converter when the current inner loop and the power outer loop are considered: f (E) q0 ,θ,P ref ,Q ref )＝0，P ref ,Q ref Respectively setting the active power and the reactive power; e (E) q0 The expression for θ is:
the expressions (8), (10), (18) and (20) are the relations between the following variables:
①I dref0 ～E q0
②I qref0 ～E d0
③I dref0 ～E q0 ,E d0
④I qref0 ～E q0 ,E d0
wherein the formulae (8), (10), (18) and (20) correspond to the formulae (1), (2), (3) and (4), respectively, and represent the relations between the variables;
the aim is to obtain E q0 The expression for θ is specifically as follows:
simultaneous (2) (4) to obtain E d0 ～E q0 The method comprises the following steps:
and then (1) and (3) are combined to obtain E d0 ～E q0 The method comprises the following steps:
to simplify the expression, set
From (22), E d0 Can be expressed as:
substituting formula (22) into formula (21) to eliminate E d0 The method comprises the following steps:
f (24) is f (E) q0 ,θ,P ref ,Q ref ) =0, denote E q0 Expression for θ.
Is provided withb＝-2a 1 k 1 k 2 -(1-2a 1 )k 2 Vcosθ-(1-2a 1 )Vsinθ-k 2 k 3 ，Equation (24) is a unitary quadratic equation:
the root formula of the unitary quadratic equation is obtained:
if the reactive ring is ignored, i.e. q=q is considered constant ref E obtained by only the simultaneous equations (1), (2) and (3) q0 The expression for θ is:
the post-reactive ring expression is far more complex to consider than if the reactive ring were not considered, and therefore the reactive ring would complicate the large disturbance stability problem of the converter.
5) Design criteria:
according to the phase-locked loop mathematical model, the analog synchronous machine adopts the equal area rule to analyze as follows:
splitting as input into two parts, consider +.>As a feed-forward quantity corresponds to the effect of the mechanical torque, whereas +.>The negative feedback amount corresponds to the electromagnetic torque. According to the equal area rule, when it is desired that the electromagnetic torque is not changed, the mechanical torque is as small as possible to reduce the acceleration area and increase the deceleration area. And E is q ＝E q1 +E q0 Wherein E is q1 Representing the component of the output of the integration section, E q0 Representing the component of the proportional direct feed link output. Suppose θ 0 Is a small angle if it is made to be within the phase angle interval [ theta ] of the converter 0 ,π-θ 0 ]Upper E q0 The reduction corresponds to the reduction of mechanical torque at large disturbances, thereby improving transient stability.
The invention has the advantages that the proportion link belongs to the direct feed link, the control speed is higher, the transient stability of the converter can be consolidated in a shorter time by adjusting the physical quantity related to the proportion link on the control scale of a fast power electronic device, the integral link is related to a plurality of factors such as the occurrence of faults, the type of faults, the system state before the faults and the like, the back mechanism is more complex and difficult to control, and in conclusion, the invention focuses on the adjustable variable in the proportion linkE q0 。
Moreover, the implementation of step 2 includes:
1) Setting an optimization target:
in step 1, design criteria are given according to a mathematical model of the converter control: hopefully, E is in the operation interval of the converter q0 As small as possible, the problem of optimizing the value of the variable over a period of time may be equivalent to the values obtained in phase angle and E, respectively q0 The area of the curve is the abscissa. Given an optimization objective function of:
the optimization variable is the proportion parameter K of each control link pn (n＝1,2,...,4)。
However, the variable values obtained at some time by adopting the optimization function are obviously unreasonable, or the optimized E appears q0 In the case of discontinuous theta curves, or in the presence of E in the operating interval q0 >A period of 0, it is therefore necessary to set a constraint so that the optimization result satisfies the actual demand.
In a specific application, the optimization objective function may be modified according to the actual requirement, and it should be noted that the optimization objective function is only one available objective function and is not the only objective function.
2) Setting limiting conditions:
the limiting conditions are set in the following according to the cases where the 3 kinds of optimization results found in 1) are not good, respectively.
First give K pn (n=1, 2,., 4) define a range,
st1:K pn_low ≤K pn ≤K pn_high (n＝1,2,...,4) (29)
wherein K is pn_high And K pn_low The upper and lower limit values of the parameters are respectively calculated according to the actual performance of the controller.
Second, E after optimization is removed q0 And the theta relationship curve is discontinuous. We equally divide the interval [ theta ] 0 ,π-θ 0 ]Is n 0 Equal parts, each partI.e. one step length, recorded as delta theta, and E of adjacent step length is calculated q0 Setting E calculated by adjacent step length q0 The difference is not more than the margin delta, namely
st2:|E q0 (θ n +Δθ)-E q0 (θ n )|≤δ (30)
Wherein, the value of the margin delta depends on the step length delta theta, and when the delta theta is reduced, the delta is reduced so as to ensure that the effective constraint function meets the continuity condition. E (E) q0 (θ n ) Representing the internal potential q-axis proportional component at the nth step.
From the above analysis, it can be seen that if E exists in the converter operation interval q0 >0, meaning that at some point the mechanical torque increases instead, which is detrimental to the stability of the converter large disturbances, our constraint should be set to constant E over the possible operating interval q0 <0. In a specific embodiment, in [ theta ] 0 ,π-θ 0 ]Sequentially calculating E in each step q0 If there is E in one step q0 >0, then the result re-optimization calculation is discarded. Described by mathematical expressions as
st3:E q0 (θ n )<0(n＝1,2,...,n 0 ) (31)
In practical applications, constraints may be increased, decreased or modified according to specific needs, where only one set of available reference constraints is provided.
The complete optimized model is as follows:
the optimization variable of the optimization model is the proportion parameter K of each control link pn (n＝1,2,...,4)。
Moreover, the implementation of step 3 includes:
1) Selecting an algorithm;
for the solution of the optimization model, a genetic algorithm (Genetic Algorithm, GA), a particle swarm algorithm (Particle Swarm Optimization, PSO) and a MATLAB nonlinear tool box are available for selection, and the demand of flexibly modifying codes according to actual demands cannot be met because MATLAB nonlinear optimization tool box algorithm codes belong to black boxes.
In a certain range, compared with a genetic algorithm, the particle swarm algorithm has a large number of random value-taking steps, and the optimization result of the genetic algorithm is easily limited by the parent value-taking, and can only be got rid of through mutation, so that the particle swarm algorithm can find the global optimal value more easily, and the efficiency is higher. And the genetic algorithm is more suitable for solving the problem of discrete variables, while the particle swarm algorithm is more suitable for solving an optimization model of continuous variables, and we study the continuous changes of the variables in the problem. Based on the reasons, the particle swarm algorithm is finally selected for optimization.
The central idea of the particle swarm algorithm is to simulate the process of finding the most food intensive place in a forest for a group of birds, firstly, randomly initializing the position of each bird, then in each step, determining the searching direction by each bird according to the experience of the bird and the information shared by the current bird swarm, and finally, each bird reaches the place with the most food, namely the local optimal solution, and the local optimal solutions are combined to obtain the global optimal solution.
2) Solving an optimization model by applying a particle swarm algorithm:
PSO initializes as a population of random particles (random solution). The particles in the particle swarm algorithm have only two properties: speed, which represents the speed of movement, and position, which represents the direction of movement.
Assuming that the target search space is D-dimensional and the number of particles is N, the position of each particle is a D-dimensional vector
X i ＝(x i1 ,x i2 ,...,x iD ),i＝1,2,...N (33)
The speed of flight is also a D-dimensional vector:
V i ＝(v i1 ,v i2 ,...,v iD ),i＝1,2,...,N (34)
and then find the optimal solution through iteration. In each iteration, the particle passes through two "tracks"Extremum "(P) best ，g best ) To update itself.
Wherein P is best Representing the optimal position currently searched by the ith particle, namely the individual extremum:
P best ＝(p i1 ,p i2 ,...,p iD ),i＝1,2,...,N (35)
g best representing the optimal position so far searched for by the whole population of particles, i.e. the global extremum:
g best ＝(g 1 ,g 2 ,...,g D ) (36)
after finding these two optimal values, the particle updates its own velocity and position by the following formula.
Wherein c 1 And c 2 Is a learning factor, also called acceleration constant; r is (r) 1 And r 2 Is in the range of [0,1 ]]The uniform random number in the system determines the randomness of the example flight; v ij Is the velocity of the particles, v ij ∈[-v max ,v max ]，v max Is a constant for which the limit particle velocity is set by the user.
The speed updating formula consists of three parts, wherein the first part is a memory term part which represents the trend of maintaining the original speed of the particles; the second part is a self-cognition part and represents the memorization of particles to self-history experience, and the trend of the history optimal position is searched; the third part is a group cognitive term, reflects the synergy and sharing among particles, and represents the trend of the particles approaching to the optimal position of the group history. The particles determine the next movement by their own experience and the best experience among peers. Based on the two formulas above, a standard form of PSO is formed.
The velocity formula of the particle swarm algorithm can also be the following
Compared with the prior art, the inertia factor omega is added, the value is not negative, and when the inertia factor is large, the global optimizing capability of the algorithm is strong, and the local optimizing capability is weak; when the inertia factor value is small, the global optimizing capability of the algorithm is weak, and the local optimizing capability is strong. The introduction of ω greatly improves the performance of the PSO algorithm, making it successfully applicable to many practical problems.
The particle swarm algorithm is based on the concepts of population and evolution, and realizes the search of the optimal solution of the complex space through cooperation and competition among individuals, and the process is as follows:
(1) initializing a population size N of the population of particles, a position X of each particle i And velocity V i Performing constraint condition processing;
(2) calculating a fitness value fit (i) of each particle;
(3) for each particle, its fitness value fit (i) and individual extremum P best (i) And (5) comparing. If fit (i)>P best (i) Then replace P with fit (i) best (i)；
(4) For each particle, use its fitness value fit (i) and global extremum g best And (5) comparing. If fit (i)>g best Then replace g with fit (i) best ；
(5) Iteratively updating velocity V of particles i And position X i ；
(6) Performing constraint condition processing and boundary condition processing;
(7) judging whether an algorithm termination condition is satisfied: if yes, ending the algorithm and outputting an optimization result; otherwise, returning to the step (2).
Furthermore, a set of constraints may be set in the algorithm:
after initializing or updating the particle velocity position, judging whether the particle meets the constraint condition, if not, randomly generating a numerical value in the range to replace and verify again until the constraint condition is met.
3) And (5) visualizing and correcting results:
after obtaining a result through particle swarm optimization, drawing optimized E q0 And judging whether the value of the optimized variable and the characteristics of the curve meet the requirements or not according to the theta relation curve, and if the value of the optimized variable and the characteristics of the curve do not meet the requirements, properly correcting the optimized objective function and constraint conditions so as to finally obtain a satisfactory result.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the method and the device for improving the stability of the large disturbance of the converter based on the particle swarm algorithm are realized when the processor executes the program.
The invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor implements any of the methods and apparatus for improving the stability of large disturbances of a current transformer based on a particle swarm algorithm described above.
The invention also provides a computer program product, which comprises a computer program, wherein the computer program is executed by a processor to realize the method and the equipment for improving the stability of the large disturbance of the converter based on any particle swarm algorithm.
Compared with the prior art, the invention has the beneficial effects that:
modeling a typical grid-connected converter control structure, comprehensively considering the influence of a power outer loop and a current inner loop on the transient stability of a phase-locked loop, deducing a corresponding relational expression, and providing a design criterion; based on design criteria and converter limitations, a particle swarm algorithm is adopted to optimize the proportion parameters of the grid-connected converter controller. The mechanism behind the proportion parameters is easy to understand, and the proportion links are quickly adjusted, so that the obtained optimized parameters are favorable for the large disturbance stability of the grid-connected converter.
The method for improving the stability of the large disturbance of the current transformer is explored from the mechanism angle, so that the problem of the stability of the large disturbance of the current transformer can be solved, and a reference basis is provided for parameter setting of a controller of the current transformer; on the other hand, the transient stability of the novel system can be improved, and the novel system is a foundation for realizing the large-scale access of new energy sources to the power grid.
The invention takes the improvement of the large disturbance stability of the system as a guide, and is different from the prior control optimization method which takes the small disturbance stability or the reliability as the target converter, the optimization objective function in the invention is obtained based on the theoretical analysis of a control model and an equal area rule. The invention aims at a converter, considers the interaction between the converter and a power grid, and can help the system to maintain stability under large disturbance by modifying the proportion parameters.
The invention derives the relation between the converter proportion parameter and the system large disturbance stability based on the formula and expounds the mechanism, and finally uses the conclusion of the mechanism analysis for practical use by a particle swarm algorithm. The invention can be used for power electronic converters in power systems in need of improving stability of large disturbance.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a topology and control block diagram of an embodiment of the present invention connected to a power grid with a grid-type converter;
FIG. 2 (a) is a flowchart of a main program of a constraint-containing multi-element optimization particle swarm algorithm according to an embodiment of the present invention;
FIG. 2 (b) is a constraint flow chart of a constraint-containing multivariate optimization particle swarm algorithm according to an embodiment of the present invention;
FIG. 3 is a graph showing the fitness evolution of a particle swarm optimization algorithm according to an embodiment of the present invention;
FIG. 4 is a diagram of the system E under the scale parameters after optimization in accordance with an embodiment of the present invention q0 -a θ curve;
fig. 5 (a) is a phase-locked loop phase angle curve of the parameter-optimized pre-converter at a voltage sag fault duration of 0.27s according to an embodiment of the present invention;
fig. 5 (b) is a phase-locked loop phase angle curve of the current transformer after parameter optimization when the duration of the voltage sag fault is 0.27s according to the embodiment of the present invention;
fig. 6 (a) is a phase angle curve of the phase-locked loop of the converter before parameter optimization when the duration of the voltage sag fault is 1.47s according to the embodiment of the invention;
fig. 6 (b) is a phase angle curve of a phase-locked loop of the converter after parameter optimization when the duration of the voltage sag fault is 1.47s according to the embodiment of the invention;
fig. 7 is a schematic structural diagram of an electronic device provided by the present invention;
wherein: 810-processor, 820-communication interface, 830-memory, 840-communication bus.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Therefore, the invention aims at the problems, comprehensively considers the current inner ring and the current outer ring from the angles of mechanism analysis and formula derivation, and reveals the mechanism that the proportion parameters of the converter influence the stability of the large disturbance. And the proportional coefficient of the converter is set by adopting a particle swarm algorithm, so that the large disturbance stability of the system is improved.
In the method for improving the stability of the large disturbance of the current transformer based on the particle swarm algorithm, a researched system and control are shown in fig. 1. The topology analysis large disturbance stability mechanism aiming at the situation that the grid-connected converter is connected with an infinite power grid is studied, the resistance is ignored, and only the reactance is considered. Wherein the converter control adopts active/reactive power outer loop control and current inner loop control. The phase angle of the voltage at the tracking end of the grid-connected converter is used as a self reference angle through a phase-locked loop, and the specific angle relation is shown in a coordinate system in figure 1. The equivalent transformed second-order model can be obtained by transforming the traditional phase-locked loop second-order model, and the transformed phase-locked loop model is more beneficial to subsequent analysis. The method comprises the following steps:
firstly, considering the influence of a power outer loop and a current inner loop on a phase-locked loop, modeling a typical grid-following converter control structure, analyzing the influence of a control loop on the transient stability of the grid-following converter, deducing a corresponding relation expression, and providing a design criterion;
secondly, a set of available optimization targets and constraint conditions are provided for the improvement of the large disturbance stability of the converter by combining design criteria and the limitation of the converter, and the optimization targets and constraint conditions are properly corrected according to specific analysis of specific situations in practical application;
and thirdly, optimizing the proportional link parameters controlled by the grid-type converter by adopting a particle swarm algorithm according to the set optimization target and constraint conditions, drawing a key variable relation curve of the converter based on an optimization result, and correcting the optimization target and constraint conditions based on an effect curve.
The analog synchronous machine adopts the equal area rule analysis,can be broken down into two parts as input, can be considered +.>As a feed-forward quantity corresponds to the effect of the mechanical torque, whereas +.>The negative feedback amount corresponds to the electromagnetic torque. According to the equal area rule, when it is desired that the electromagnetic torque is not changed, the mechanical torque is as small as possible to reduce the acceleration area and increase the deceleration area. And E is q ＝E q1 +E q0 Wherein E is q1 Representing the component of the output of the integration section, E q0 Representing the component of the proportional direct feed link output. Suppose θ 0 Is a small angle if designed such that at variable flowPhase angle interval [ theta ] 0 ,π-θ 0 ]Upper E q0 The reduction is equivalent to the reduction of mechanical torque at the time of large disturbance, and the transient stability is improved.
The proportion parameter K of each control link pn (n=1, 2,.,. 4) as optimization variable, it is desirable to E in the converter operating interval q0 As small as possible, the problem of optimizing the value of the variable over a period of time may be equivalent to the values obtained in phase angle and E, respectively q0 The area of the curve is the abscissa. Given an optimized objective function ofA flowchart of the main algorithm for solving the optimization model under constraint conditions is shown in fig. 2 (a). And finally, the improvement of the large disturbance stability of the converter is realized by mechanism analysis, establishment of an optimization model and solving of the optimization model.
In the implementation, in the first step, the transformed phase-locked loop model is firstly deduced according to the phase-locked loop second-order model and the circuit model, namely, the equation (4) is obtained through the equations (1) and (2). From the transformed phase-locked loop model of FIG. 1, E is obtained using equal area rule analysis q0 The smaller the large disturbance stability, the better the conclusion.
And then a model of the controller only aiming at the proportional link is established according to a converter control block diagram:
simultaneous obtaining E q0 The expression for θ is
Wherein,
the required parameters are shown in Table 1.
Table 1 converter access grid model parameters
In the implementation, in the second step, according to the design criteria set forth in the first step: suppose θ 0 Is a small angle if designed such that it is within the phase angle interval [ theta ] of the converter 0 ,π-θ 0 ]Upper E q0 The reduction is equivalent to the reduction of mechanical torque in the case of large disturbance, the improvement of transient stability, and the optimization problem of the values of variables in a section interval is equivalent to the optimization problem of the values of the variables in a section interval by phase angle and E respectively q0 The curve area optimization problem for the abscissa and the ordinate. With the proportion parameter K of each control link pn (n=1, 2,.,. 4) as an optimization variable, an optimization objective function is set as
To exclude the following phenomena:
(1) the variable value obtained when the optimization function is adopted is obviously unreasonable;
(2) appearance of optimized E q0 The discontinuous condition of the theta relation curve;
(3) presence of E in the operating interval q0 >0.
Setting such a set of constraints:
give K pn (n=1, 2,., 4) define a range,
st1:K pn_low ≤K pn ≤K pn_high (n＝1,2,...,4) (44)
wherein K is pn_high And K pn_low The upper and lower limit values of the parameters are respectively obtained according to the actual performance of the controller.
Equally dividing section [ theta ] 0 ,π-θ 0 ]Is n 0 Equal parts, each part being a step length, denoted as delta theta, withE for calculating adjacent step sizes q0 Setting E calculated by adjacent step length q0 The difference is not more than the margin delta, namely
st2:|E q0 (θ n +Δθ)-E q0 (θ n )|≤δ (45)
Wherein, the value of the margin delta depends on the step length delta theta, and when the delta theta is reduced, the delta is reduced so as to ensure that the effective constraint function meets the continuity condition. E (E) q0 (θ n ) Representing the internal potential q-axis proportional component at the nth step.
In [ theta ] 0 ,π-θ 0 ]Sequentially calculating E in each step q0 If there is E in one step q0 >0, then the result re-optimization calculation is discarded. Described by mathematical expressions as
st3:E q0 (θ n )<0(n＝1,2,...,n 0 ) (46)
In practical applications, constraints may be increased, decreased or modified according to specific needs, where only one set of available reference constraints is provided.
Comprehensive analysis, the complete optimized model is as follows:
the optimization variable is the proportion parameter K of each control link pn (n＝1,2,...,4)。
The parameters required for optimizing the model are shown in table 2:
table 2 optimization of model parameters
In specific implementation, according to the optimization model selection algorithm, the optimization model provided in the second step is a continuous optimization variable, multiple constraint optimization problems are included, and flexible adjustment of objective functions and constraint conditions is expected, so that the optimization model is solved by adopting a particle swarm algorithm.
The constraint flow chart is shown in fig. 2 (b):
(1) initializing a population size N of the population of particles, a position X of each particle i And velocity V i Performing constraint condition processing;
(2) calculating a fitness value fit (i), i.e. a current objective function value, of each particle;
(3) for each particle, its fitness value fit (i) and individual extremum P best (i) And (5) comparing. If fit (i)>P best (i) Then replace P with fit (i) best (i)；
(4) For each particle, use its fitness value fit (i) and global extremum g best And (5) comparing. If fit (i)>g best Then replace g with fit (i) best ；
(5) Iteratively updating velocity V of particles i And position X i ；
(6) Performing constraint condition processing and boundary condition processing;
(7) judging whether an algorithm termination condition is satisfied: if yes, ending the algorithm and outputting an optimization result; otherwise, returning to the step (2).
A flow chart of the particle swarm algorithm with the constraint condition is shown in fig. 3.
The parameters required by the particle swarm algorithm are shown in Table 3:
table 3 particle swarm optimization algorithm parameters
The optimization results are shown in Table 4. FIG. 3 is an adaptive evolution curve, FIG. 4 is E under optimized scale parameters q0 -a theta curve.
TABLE 4 optimization results
Simulation in topology of single transformer access power grid, real settingThe experimental group and the control group, wherein the parameters of the control group are taken from a reference document, the integral parameters of the experimental group are kept unchanged, the proportion parameters are optimized parameters (the upper limit is set to be 50), and the values of the parameters are shown in table 5. Set at t 0 A dip in the grid voltage as a large disturbance (which is possible in future weak grids) occurs at time=50s, with a dip amplitude of 0.3p.u., i.e. the pre-fault grid voltage v=1p.u., and the post-dip grid voltage V 0 =1p.u. Critical fault clearing times were observed for both sets of parameters. The critical fault clearing time of the parameters before and after optimization is obtained through simulation and is respectively 0.27s and 1.47s, the simulation result is shown in table 6, and the curves of the phase angle (namely the phase angle of the voltage of the converter end) tracked by the converter through the phase-locked loop under the critical condition are shown in fig. 5 (a), fig. 5 (b), fig. 6 (a) and fig. 6 (b). Therefore, the critical fault clearing time after parameter optimization is longer, the large disturbance stability is stronger, and the critical fault clearing time is consistent with the analysis result of the mechanism, thereby proving the effectiveness of parameter optimization.
Table 5 converter control PI link parameter values
TABLE 6 simulation results
In summary, the embodiment is based on the modeling of the typical grid-connected converter control structure, comprehensively considers the influence of the power outer loop and the current inner loop on the transient stability of the phase-locked loop, deduces a corresponding relational expression, and proposes a design criterion; and the design criterion and the parameter limitation of the converter are combined, the proportion parameter of the following-net type converter controller is optimized by adopting a particle swarm algorithm, the design criterion aims at improving the stability of the large disturbance of the converter, and a reference basis is provided for parameter setting of the converter controller.
Fig. 7 illustrates a physical schematic diagram of an electronic device, as shown in fig. 7, which may include: processor 810, communication interface (Communications Interface) 820, memory 830, and communication bus 840, wherein processor 810, communication interface 820, memory 830 accomplish communication with each other through communication bus 840. The processor 810 may invoke logic instructions in the memory 830 to perform a method that promotes stability of large disturbances of the converter based on particle swarm optimization.
Further, the logic instructions in the memory 830 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention further provides a computer program product, where the computer program product includes a computer program, where the computer program can be stored on a non-transitory computer readable storage medium, and when the computer program is executed by a processor, the computer can execute the method for improving the stability of the large disturbance of the converter based on the particle swarm algorithm provided by the above methods.
In yet another aspect, the present invention further provides a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, is implemented to perform the method for improving the stability of a large disturbance of a current transformer based on a particle swarm algorithm provided by the above methods.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. The method for improving the stability of the large disturbance of the converter based on the particle swarm optimization is characterized by comprising the following steps:
building a control structure model of the follow-net type converter, obtaining a corresponding relational expression, and providing a design criterion;
setting a transient stability optimization target and constraint conditions of the converter by combining design criteria and limitation of the converter;
according to the set transient stability optimization target and constraint conditions of the converter, the proportional link parameters controlled by the grid-type converter are optimized by adopting a particle swarm algorithm, a key variable relation curve of the converter is drawn based on an optimization result, and the optimization target and constraint conditions are corrected based on an effect curve.
2. The method for improving the stability of the large disturbance of the current transformer based on the particle swarm optimization according to claim 1, wherein the step of establishing a control structure model of the grid-connected current transformer to obtain a corresponding relational expression and providing design criteria comprises the following steps:
s1.1, a phase-locked loop mathematical model is as follows:
defining the amplitude of the internal potential of the current transformer as E and the phase angle as delta, transforming a classical phase-locked loop into a phase-locked loop with V according to the following formula Gq As input, θ is the form of output;
wherein V is Gq The phase-locked loop input quantity is theta, and the phase angle of the converter output by the phase-locked loop is theta; e (E) q Is a component of the input quantity, and the control loop adjusts the internal potential q-axis component E q Further adjusting theta; v is infinite power supply voltage amplitude, infinite power supply voltage phase angle is 0 degree, X L The line reactance is the sum of the line reactance and the reactance in the converter;
s1.2, a mathematical model of an inner loop of current:
the d-axis current inner loop mathematical model is as follows:
wherein K is p1 And K i1 Respectively representing the proportional parameter and the integral parameter of the d-axis current loop, I dref Representing the d-axis current set point, X G Representing reactance in the converter, s being a complex variable in the Laplace transform;
extracting the expression related to the proportion links as follows:
wherein, subscript 0 represents the component output by the proportional link, subscript 1 represents the component output by the integral link, and subscripts d and q of the variables respectively represent the components under the reference coordinate system of the phase-locked loop; e (E) q0 Representing components output by a proportional direct feed link;
the mathematical model of the q-axis current inner loop is as follows:
wherein E is d Representing the d-axis component, K, of the internal potential p2 And K i2 Respectively representing the proportional parameter and the integral parameter of the d-axis current loop, I qref Representing the q-axis current set point;
extracting the expression related to the proportion links as follows:
s1.3, a power outer loop mathematical model:
the mathematical model of the active power outer loop is:
wherein K is p3 And K i3 Respectively representing a proportion parameter and an integral parameter of the active power loop;
the extraction of the part which is only related to the proportion links is as follows:
the mathematical model of the reactive power outer loop is:
wherein K is p4 And K i4 Respectively representing the proportional parameter and the integral parameter of the reactive power loop;
extracting the part which is only relevant to the proportion link is available:
s1.4, obtaining a relational expression of an internal potential q-axis proportion component and a converter phase angle position in consideration of a current inner loop and a power outer loop: f (E) q0 ,θ,P ref ,Q ref )＝0，P ref ,Q ref Respectively setting the active power and the reactive power; e (E) q0 The expression for θ is:
wherein,b＝-2a 1 k 1 k 2 -(1-2a 1 )k 2 V cosθ-(1-2a 1 )V sinθ-k 2 k 3 ，
s1.5, the design criteria are as follows:
setting an angle theta 0 So that in the phase angle interval [ theta ] of the converter 0 ,π-θ 0 ]Upper E q0 Reduction, achieving a reduction in mechanical torque at large disturbancesSo as to improve the transient stability of the converter.
3. The method for improving the stability of the large disturbance of the current transformer based on the particle swarm optimization according to claim 1, wherein the setting of the transient stability optimization target and the constraint condition of the current transformer by combining the design criterion and the limitation of the current transformer comprises the following steps:
s2.1, setting an optimization target;
the optimization objective function is:
the optimization variable is the proportion parameter K of each control link pn ,n＝1,2,...,4；
S2.2, setting limiting conditions:
setting a limiting condition aiming at the condition that the optimization result is not good; if the variable value obtained by adopting the optimization objective function is obviously unreasonable, or the optimized E appears q0 Discontinuous theta curve or E existing in running interval of current transformer q0 >A period of 0; the limiting conditions are set as follows:
to K pn N=1, 2,..4 values define the range:
st1:K pn_low ≤K pn ≤K pn_high ,n＝1,2,...,4
wherein K is pn_high And K pn_low Respectively the upper limit value and the lower limit value of the parameter, and taking a value according to the actual performance of the controller;
e after removal of optimization q0 The average division interval [ theta ] under the condition that the theta relation curve is discontinuous 0 ,π-θ 0 ]Is n 0 Equal parts, each part is a step length, which is marked as delta theta, and E of adjacent step lengths is calculated q0 Setting E calculated by adjacent step length q0 The difference is not more than a margin delta, described by the expression:
st2:|E q0 (θ n +Δθ)-E q0 (θ n )|≤δ
wherein the margin delta is determined by the stepWhen the length delta theta is reduced, the delta is also reduced, E q0 (θ n ) Representing the internal potential q-axis proportional component at the nth step size;
if E exists in the operation interval of the converter q0 >Period of 0, at [ theta ] 0 ,π-θ 0 ]Sequentially calculating E in each step q0 If there is E in one step q0 >0, then the result re-optimization calculation is discarded; described by a mathematical expression:
st3:E q0 (θ n )<0,n＝1,2,...,n 0
the optimized model is as follows:
the optimization variable of the optimization model is the proportion parameter K of each control link pn ,n＝1,2,...,4。
4. The method for improving the stability of the large disturbance of the current transformer based on the particle swarm optimization according to claim 1, wherein the optimization of the proportional link parameters controlled by the grid-type current transformer by using the particle swarm optimization comprises the following steps:
s3.1. Initializing a population size N of the population of particles, a position X of each particle i And velocity V i Performing constraint condition processing;
s3.2, calculating a fitness value fit (i) of each particle;
s3.3. The fitness value fit (i) and the individual extremum P of each particle best (i) Comparing; if fit (i)>P best (i) Then replace P with fit (i) best (i)；
S3.4, the fitness value fit (i) and the global extremum g of each particle are calculated best Comparing; if fit (i)>g best Then replace g with fit (i) best ；
S3.5, iteratively updating the velocity V of the particles i And position X i ；
S3.6, constraint condition processing and boundary condition processing are carried out;
s3.7, judging whether an algorithm termination condition is met: if yes, ending the algorithm and outputting an optimization result; otherwise return S3.1.2;
setting constraint conditions in an algorithm:
after initializing or updating the particle velocity position, judging whether the particle meets the constraint condition, if not, randomly generating a numerical value in the range to replace and verify again until the constraint condition is met.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of improving the stability of large disturbances of a current transformer based on a particle swarm algorithm according to any of claims 1 to 4 when executing the program.
6. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a method of improving the stability of a large disturbance of a current transformer based on a particle swarm algorithm according to any of claims 1 to 4.
7. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements a method for improving the stability of a large disturbance of a current transformer based on a particle swarm algorithm according to any of claims 1 to 4.
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